Specifies how the genetic component selects individuals from the current generation to be used as parents of the next generation. The genetic component provides some built-in methods and allows you to write your own by using the
OnSelection event.
Namespace: RSGeneticBase
Delphi
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public
property SelectionMethod: TGASelectionMethod read FSelectionMethod write SetSelectionMethod default smStochasticTournament;
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Property Value
Type: TGASelectionMethod
The following example runs the genetic algorithm component using the different selection methods to solve the N queens problem:
Delphi
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procedure TForm1.btnRunClick(Sender: TObject);
var
SavePop: TRSGAPopulation;
Gen: Integer;
begin
Screen.Cursor := crHourGlass;
// save population as is
SavePop := TRSGAPopulation.Create(nil, RSGeneticAlgorithm1.Population.ItemClass);
try
if not RSGeneticAlgorithm1.Initialized then
RSGeneticAlgorithm1.Initialize;
SavePop.Assign(RSGeneticAlgorithm1.Population);
Gen := RSGeneticAlgorithm1.Generation;
FCurrentSeries := RouletteSeries;
RunSelectionMethod( smRoulette, Gen, SavePop );
FCurrentSeries := RandomSeries;
RunSelectionMethod( smRandom, Gen, SavePop );
FCurrentSeries := TournamentSeries;
RunSelectionMethod( smTournament, Gen, SavePop );
FCurrentSeries := StochasticTournamentSeries;
RunSelectionMethod( smStochasticTournament, Gen, SavePop );
FCurrentSeries := ElitistSeries;
RunSelectionMethod( smElitist, Gen, SavePop );
finally
Screen.Cursor := crDefault;
SavePop.Free;
end;
end;
procedure TForm1.RunSelectionMethod( SelectionMethod: TGASelectionMethod; CurrentGeneration: Integer; InitialPopulation: TRSGAPopulation );
var
i: Integer;
begin
RSGeneticAlgorithm1.SelectionMethod := SelectionMethod;
RSGeneticAlgorithm1.Generation := CurrentGeneration;
RSGeneticAlgorithm1.Population := InitialPopulation;
RSGeneticAlgorithm1.Evolve(StrToInt(Edit1.Text));
// print statistics
with RSGeneticAlgorithm1 do
begin
Memo1.Lines.Add('');
Memo1.Lines.Add('Selection Method: '+GetEnumName(TypeInfo(TGASelectionMethod), Ord(RSGeneticAlgorithm1.SelectionMethod)));
Memo1.Lines.Add('Number of generations:' + IntToStr(Generation));
Memo1.Lines.Add('Fittest Individual:' + FittestIndividual.Bits.ToString);
Memo1.Lines.Add(' Score:' + FloatToStr(RSGeneticAlgorithm1.FittestIndividual.Fitness));
Memo1.Lines.Add('Avg Fitness:' + FloatToStr(AvgFitness));
Memo1.Lines.Add('Min Fitness:' + FloatToStr(MinFitness));
Memo1.Lines.Add('Max Fitness:' + FloatToStr(MaxFitness));
if MaxFitness = FitnessCutoff then
for i := 0 to RSGeneticAlgorithm1.Genes.Count - 1 do
begin
sgChessBoard.Cols[i].Clear;
sgChessBoard.Cells[i, RSGeneticAlgorithm1.Genes[i].AsInteger[RSGeneticAlgorithm1.FittestIndividual.Index]] := 'Q';
end;
end;
end;
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